Real-time digital twin technology can enhance traffic safety of intelligent vehicular system and provide scientific strategies for intelligent traffic management. At the same time, real-time digital twin depends on strong computation from vehicle side to cloud side. Aiming at the problem of delay caused by the dual dependency of timing and data between computation tasks, and the problem of unbalanced load of mobile edge computing servers, a parallel intelligence-driven resource scheduling scheme for computation tasks with dual dependencies of timing and data in the intelligent vehicular systems (IVS) is proposed. First, the delay and energy consumption models of each computing platform are formulated by considering the dual dependence of sub-tasks. Then, based on the bidding idea of the auction algorithm, the allocation model of computing resources and communication resources is defined, and the load balance model of the mobile edge computing (MEC) server cluster is formulated according to the load status of each MEC server. Secondly, joint optimization problem for offloading, resource allocation, and load balance is formulated. Finally, an adaptive particle swarm with genetic algorithm is proposed to solve the optimization problem. The simulation results show that the proposed scheme can reduce the total cost of the system while satisfying the maximum tolerable delay, and effectively improve the load balance of the edge server cluster.

Virtual reality (VR) is an important landing scenario for 5G and a key enabling technology for the digital economy, and the core challenge it faces is how to ensure real-time interaction and content distribution efficiency. Existing solutions rely on adaptive delivery, overall content edge caching, or asynchronous rendering in the cloud, with drawbacks such as high caching and computing costs and difficulty in ensuring real time. This article is oriented to content generation and synchronization of virtual reality, and aims at realizing intelligent transmission of VR content with high efficiency and low delay guarantee, and carries out research on cloud-edge-end collaborative mechanisms. First, for AI-aided VR content generation, a cloud-edge-end collaborative service architecture is constructed to support independent encoding and distribution of background and interactive content generation. Second, to address the issue that the MEC node with limited cache space cannot meet the dynamical demands of VR user, a cloud-edge collaborative caching strategy based on graph neural networks is proposed to achieve optimal caching and updating of background content, in which the background content is first cached MEC node according to the content request with node sharing scheme, then the content is updated according to a minimal cost update algorithm based on the graph neural networks (GNN)-aided request prediction. Finally, the proposed algorithm is simulated and tested, the simulation results show that the proposed caching and update algorithm achieve better quality of experiment (QoE) and higher cache hit ratio compared with comparison algorithms.

The Finance-level Artificial Intelligence of Things (AIoT) is going to become a novel media in the 6G-driven digital society. Inside the financial AIoT environment, large-scale crowd credit assessment with the guarantee of low latency has been a general demand. Facing limited computational resources, there is still a lack of effective computation offloading methods for this purpose to ensure low latency. In order to deal with such an issue, this paper introduces edge computing mode and proposes a low-latency edge computation offloading scheme for trust evaluation in financial AIoT. With different elements involved in the assessment process being denoted via mathematical description, a multi-objective optimization problem with constraints is formulated. Then, the aforementioned optimization problem is solved by a specific search algorithm, so that optimal task offloading schemes can be found. To assess the performance of the proposal, some simulation experiments are conducted to verify the proposed task offloading method. And it can be reflected from numerical results that latency can be well reduced compared with baseline methods.

Vehicular Named Data Networking (V-NDN) is promising to improve the content delivery efficiency in Vehicular Ad Hoc Networks (VANETs). However, the potential broadcast storm caused by Interest packet flooding and return path failures caused by vehicle mobility can significantly degrade the content delivery performance. Existing forwarding strategies based on outdated position information cannot address these issues well. In this paper, we propose a novel predictive forwarding strategy (PRFS) for V-NDN. In PRFS, Long Short-Term Memory (LSTM) is employed to amend the NeighBor Table (NBT) for preciser neighboring vehicles' positions. In addition, the next-hop forwarder is selected among the neighbors, taking into account the link reliability and the Distance along the Road (DR) in both directions. Furthermore, a new mechanism is designed to notify the selected next-hop forwarder by embedding the forwarder identity in the Interest packet header, so as to accelerate the forwarding process. Finally, extensive simulations are carried out, and experimental results demonstrate that PRFS can reduce the number of forwarded Interest packets and data packets by 21.29% and 25.75%, respectively, and improve the success ratio of satisfied Interest packets by 35.1% compared to the existing baseline algorithms.

Uploading Virtual Reality (VR) video over cellular networks is expected to boom in near future, as general consumers could generate high-quality VR videos with portable 360-degree cameras and are willing to share with others. Consequently, concerns of uplink bandwidth and delay arose for current popular technology of tile-based VR video streaming, which requires high quality video to transcode into multiple representations for further adaptive streaming. Motivated by this, we proposed a novel scheme for uplink delivery of tile-based VR video over cellular network, in which encoding bit rate of each tile is determined by the uplink resource allocation (RA), and quality of content (QoC) contribution of each tile and channel quality of user equipments (UEs) are jointly considered during RA. Moreover, the RA problem is formulated as a frequency and time dependent non-deterministic polynomial (NP)-hard problem. We further propose three algorithms to explore solving the RA problem. Simulation results show that the proposed approximate convex algorithm with low complexity can achieve higher utility, that is higher total quality of experience (QoE) for viewers.